OpenGenes MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: one queries the database, one provides example queries, and one gives schema information. An agent can easily tell them apart based on their specific functions.
Naming Consistency5/5All tools follow a consistent 'opengenes_' prefix with descriptive suffixes (_db_query, _example_queries, _get_schema_info), using snake_case uniformly. This predictable pattern enhances readability and usability.
Tool Count3/5With only 3 tools, the set feels thin for a database query server, as it lacks operations like data manipulation (e.g., insert, update) or advanced query features. However, it covers basic query support adequately for its stated purpose.
Completeness3/5The tools provide essential query, example, and schema functions, but there are notable gaps such as no data modification tools (e.g., create, update, delete) or specialized query filters. This limits the server's ability to handle full database workflows.
Average 3.2/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information, implying a read-only operation, but doesn't specify details like authentication needs, rate limits, or what the output contains. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to parse. However, it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is minimally complete. It states what the tool does but lacks details on usage context, behavioral traits, or output specifics. For a simple tool, this is adequate but leaves room for improvement in guiding the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it doesn't incorrectly imply any parameters. A baseline score of 4 is appropriate as the description doesn't mislead about parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose ('Get information about the database schema'), which is clear but vague. It specifies a verb ('Get') and resource ('database schema'), but doesn't distinguish it from sibling tools like 'opengenes_db_query' or explain what type of schema information is retrieved. This makes it adequate but with clear gaps in specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'opengenes_db_query' or 'opengenes_example_queries', nor does it specify contexts or prerequisites for usage. This lack of comparative or contextual advice leaves the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't reveal any behavioral traits such as whether it's read-only, if it has rate limits, what the output format might be, or any authentication requirements. This leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's function without any unnecessary words. It is front-loaded and efficiently conveys the core purpose, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations) and the presence of an output schema, the description is minimally adequate. However, it doesn't provide context on why or when to use this tool over siblings, and with no annotations, it misses behavioral details that could help the agent. The output schema likely covers return values, but the description could still benefit from more contextual guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter information is needed. The description doesn't add parameter details, which is appropriate here, but it could have mentioned if there are any implicit parameters or options. Baseline is 4 for zero parameters, as the schema fully covers the lack of inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('a list of example SQL queries'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'opengenes_get_schema_info', which might also provide informational content about the database structure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as 'opengenes_db_query' for executing queries or 'opengenes_get_schema_info' for schema details. It lacks context on prerequisites or typical use cases, leaving the agent to infer usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the database content and query action, it lacks critical behavioral details: what type of queries are supported (e.g., SELECT only?), authentication requirements, rate limits, error handling, or what the output looks like (though an output schema exists). The description adds some context but is insufficient for a mutation-like tool (querying a database).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences. The first sentence clearly states the purpose, and the second provides critical usage guidance. Both sentences earn their place, though it could be slightly more front-loaded by leading with the action (query) more explicitly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (database query tool), no annotations, and 0% schema coverage, the description is moderately complete. It covers purpose and usage guidelines well, and an output schema exists to handle return values. However, it lacks details on parameter semantics and behavioral transparency, leaving gaps for the agent to infer how to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides no information about the single parameter 'sql', and schema description coverage is 0%. It doesn't explain what SQL syntax is expected, valid query types, or any constraints (e.g., read-only queries). The baseline would be lower, but the usage guideline indirectly hints at checking schema tools for parameter details, offering minimal compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to query a specific database (Opengenes) containing gene data related to longevity and aging. It specifies the database content (genes involved in longevity, lifespan experiments, aging changes) and the action (query). However, it doesn't explicitly differentiate from its siblings (opengenes_example_queries, opengenes_get_schema_info) beyond the query action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Before calling this tool the first time, always check tools that provide schema information and example queries.' This directly references the sibling tools (opengenes_example_queries, opengenes_get_schema_info) as prerequisites, giving clear when-to-use instructions and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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